Patent systems are commonly evaluated through the protection they provide to inventors, the number of applications and grants they process, and the efficiency of examination. These measures are important, but none alone establishes whether a patent regime produces an appropriate balance between innovation incentives, knowledge diffusion, follow-on innovation, competition, and institutional costs. The challenge is particularly relevant when artificial intelligence (AI) changes the cost, speed, and organization of innovation, although the direction and magnitude of these effects may differ across technological fields and firm types. This paper proposes a Dynamic Innovation-Incentive Architecture (DIIA) as a preliminary framework for comparing patent-policy instruments under different innovation conditions. Rather than treating examination intensity, application and renewal fees, licensing arrangements, competition policy, and alternative innovation incentives as universally beneficial or harmful, the framework links observable features of innovation environments to behavioral mechanisms, policy options, and welfare-relevant outcomes. Relevant features include research cost and risk, technological cycle length, ease of imitation, cumulative innovation dependence, fragmentation of complementary rights, and applicants’ resource constraints. The paper does not claim novelty for the individual policy instruments or for the established economic trade-offs underlying patent protection. Its proposed contribution is a conditional structure for policy comparison and research design. Four propositions are developed concerning examination delays, licensing frictions in cumulative technologies, heterogeneous effects of AI-assisted innovation, and the possible value of policy combinations. These propositions remain hypotheses requiring formalization and empirical testing. The paper concludes with an evaluation agenda that incorporates patent quality, follow-on innovation, diffusion, competition, administrative costs, and distributional effects.
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Lucas Xiaochun Xu (2026) studied this question.
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